NeurIPS 2026 Workshop

SaTQuML: Secure and Trustworthy Quantum Machine Learning

Venue Georgia World Congress Center (GWCC) Exhibition Hall C2, Atlanta, Georgia, United States
Workshop Date December 12, 2026
Call for Papers View Schedule SaTQuML@gmail.com

News

  • 📢 Deadline Extension: Submission portal bug is fixed and deadline is extended by 1 week (September 05, 2026 at 11:59PM AoE). We are looking forward to your submission.
  • Call for Program Committee (PC): We are actively looking for experts to join the program committee for the workshop. We encourage all interested researchers to apply, especially those from underrepresented groups. Prior reviewer experience is a nice-to-have, but not required. Interest and familiarity with subject matters related to the workshop is required. If you are interested, please fill out the application form to join us. [CLOSED]
  • Call for papers is available. Submission portal will be open at OpenReview

About

SaTQuML: Secure and Trustworthy Quantum Machine Learning brings together researchers working at the intersection of quantum machine learning (QML), cybersecurity, trustworthy AI, quantum security, and cyberdefense. As QML moves toward practical testing on hybrid quantum-classical platforms, its use in security-sensitive settings must be guided by clear benchmarks, realistic threat models, strong baselines, and careful evaluation.

The workshop focuses on two connected themes. The first is the security and trustworthiness of QML systems themselves, including robustness, privacy, reliability, interpretability, deployment risks, model leakage, and behavior under noisy hardware conditions. The second is the use of QML and hybrid quantum-classical methods for cyberdefense and related security applications, including anomaly detection, malware analysis, intrusion detection, vulnerability prioritization, cyber-physical security, and threat intelligence.

QML is entering an important transition period. Academic research groups and industry platforms are making hybrid quantum-classical experimentation increasingly accessible through open-source software, simulators, cloud-based access to quantum computers, and early application-driven demonstrations. This creates a need to evaluate QML beyond expressivity, trainability, and quantum-advantage claims, with attention to realistic data, reproducibility, robustness, privacy, security, and deployment constraints.

SaTQuML emphasizes rigorous evaluation over speculative claims. Its central goal is to help define meaningful research problems, strong classical and quantum baselines, realistic threat models, reproducible benchmarks, and shared best practices for secure and trustworthy QML systems in security-critical settings.

Key Problems We Aim to Address

Trustworthy QML Systems
Define how quantum machine learning systems should be evaluated for security, robustness, privacy, reliability, interpretability, and model leakage. Contributions may study how QML models behave under noisy hardware, limited data, adversarial manipulation, and distribution shift.
Benchmarks and Evaluation
Develop realistic benchmarks, datasets, threat models, and evaluation protocols for secure and trustworthy QML. Contributions may compare QML methods against strong classical, quantum, and quantum-inspired baselines, including negative results and studies showing when QML is useful, limited, or unlikely to help.
Cyberdefense Applications
Explore QML and hybrid quantum-classical models for cyberdefense tasks such as intrusion detection, anomaly detection, malware analysis, phishing and fraud detection, vulnerability prioritization, cyber-physical security, and threat intelligence.
Secure Deployment
Study how QML pipelines can be securely trained, deployed, and accessed in cloud, edge, quantum-cloud, and hybrid computing environments. Contributions may address deployment risks, quantum-cloud access, secure quantum computing, quantum-network security, and post-quantum cyberdefense.

Call for Papers

NeurIPS 2026 Workshop on SaTQuML: Secure and Trustworthy Quantum Machine Learning invites submissions from researchers working on secure, reliable, and realistic quantum machine learning. We welcome work on trustworthy QML systems, rigorous benchmarking, cyberdefense applications, and secure deployment. See the scope section below for the broader workshop motivation and focus.

Key Dates

  • Submission Start: August 15, 2026 (AoE)
  • Submission Deadline: August 29, 2026 (AoE) September 5, 2026 (AoE)
  • Acceptance Notification: September 29, 2026 (AoE)
  • Camera-ready Deadline: October 03, 2026 (AoE)

Submission Site

Submit papers through the SaTQuML submission portal on OpenReview.

Submission Tracks

  • Long Paper: 9 pages.
  • Short Paper: 4 pages.
  • Tiny Paper: 2 pages.

Scope

Primary themes for the workshop include:

  • Trustworthy QML Systems: Adversarial robustness, privacy, reliability, model leakage, hardware noise, and distribution shift.
  • Rigorous Benchmarks and Evaluation: Realistic datasets and threat models; strong baselines; reproducibility, ablations, and negative results.
  • QML for Cyberdefense: Intrusion, anomaly, malware, fraud, and cyber-physical defense; vulnerability prioritization and threat intelligence.
  • Secure QML Deployment: Secure training and inference across cloud, edge, quantum-cloud, and hybrid systems; remote-access and pipeline risks.

Example subtopics and concrete directions include:

  • Security and trustworthiness of QML systems, including robustness, privacy, fairness, reliability, interpretability, and model leakage.
  • Adversarial evaluation of QML, including perturbation, poisoning, evasion, extraction, quantum noise, and distribution-shift settings.
  • Security-sensitive learning methods, including quantum kernels, variational quantum circuits, quantum neural networks, quantum-inspired learning, and hybrid quantum-classical models.
  • Benchmarking and reproducibility, including realistic datasets, strong classical, quantum, and quantum-inspired baselines, ablations, and hardware-aware evaluation.
  • Cyberdefense applications such as intrusion detection, anomaly detection, malware analysis, phishing and fraud detection, cyber-physical security, and threat intelligence.
  • Practical utility and limitations of QML, including studies identifying when QML is useful, limited, or unlikely to improve over classical methods.
  • Secure deployment of QML pipelines in cloud, edge, quantum-cloud, and hybrid computing environments.
  • Quantum-era security, including post-quantum cyberdefense, quantum-network security, secure quantum computing, and trustworthy AI methods for quantum and hybrid models.

Submission Guidelines

Format:  All submissions must be a single PDF file. We accept long papers up to 9 pages, short papers up to 4 pages, and tiny papers up to 2 pages. References and appendices are not included in the page limit, but the main text must be self-contained. Reviewers are not required to read beyond the main text.

Style file:   You must format your submission using the NeurIPS 2026 LaTeX style file. Please include the references and supplementary materials in the same PDF. The maximum file size for submissions is 50MB. Submissions that violate the NeurIPS style (e.g., by decreasing margins or font sizes) or page limits may be rejected without further review.

Dual-submission and non-archival policy:  We welcome ongoing and unpublished work. We will also accept papers that are under review at the time of submission, or that have been recently accepted, provided they do not breach any dual-submission or anonymity policies of those venues. The workshop is a non-archival venue and will not have official proceedings. Workshop submissions can be subsequently or concurrently submitted to other venues.

Visibility:   Submissions and reviews will not be public. Only accepted papers will be made public.

Double-blind reviewing:   All submissions must be anonymized and may not contain any identifying information that may violate the double-blind reviewing policy. This policy applies to any supplementary or linked material as well, including code. If you are including links to any external material, it is your responsibility to guarantee anonymous browsing. Please do not include acknowledgements at submission time. If you need to cite one of your own papers, you should do so with adequate anonymization to preserve double-blind reviewing. Any papers found to be violating this policy will be rejected.

Please be AWARE:   OpenReview's moderation policy for newly created profiles in the Call for Papers: New profiles created without an institutional email will go through a moderation process that can take up to two weeks. New profiles created with an institutional email will be activated automatically.

Contact:   For any questions, please contact us at SaTQuML@gmail.com

Schedule

This is the tentative schedule of the workshop. All slots are provided in local time.

Morning Session

08:00 - 08:10 Introduction and Opening Remarks
08:10 - 08:40 Keynote Talk 1: Samuel Yen-Chi Chen, Wells Fargo
08:40 - 08:45 Break
08:45 - 09:15 Keynote Talk 2: Muhammad Usman, CSIRO
09:15 - 09:20 Break
09:20 - 09:50 Keynote Talk 3: Swaroop Ghosh, Pennsylvania State University
09:50 - 10:05 Break
10:05 - 10:15 What does it cost to trust a quantum model? Shot-priced certification for hybrid QML
Pavel Sulimov (Zurich University of Applied Sciences), Claude Lehmann (Zurich University of Applied Sciences; University of Zurich)
10:15 - 10:25 QCBA: Quantum Coalescence Backdoor Attack against Quantum Convolutional Neural Networks
Junrui Zhang (Old Dominion University), Zemin Chen (Old Dominion University), Yao Wang (Old Dominion University), Jiang Li (Old Dominion University), Chunsheng Xin (Iowa State University), Hongyi Wu (The University of Arizona), Rui Ning (Old Dominion University)
10:25 - 10:35 Responsible Quantum Adoption in Distributed Human-AI Systems: A Role-Separated Benchmark
Luyao Zhang (Duke Kunshan University)
10:35 - 10:40 QAELL: Toward Quantum-Assisted Energy-Landscape Learning for Anomaly Detection
Jingjing Wang (Beijing QBoson Quantum Technology Co., Ltd.), Chang Wu (Beijing QBoson Quantum Technology Co., Ltd.; Beijing Normal University), Chao Ju (Beijing QBoson Quantum Technology Co., Ltd.), Qi Gao (Beijing QBoson Quantum Technology Co., Ltd.), Yin Ma (Beijing QBoson Quantum Technology Co., Ltd.), Hai Wei (Beijing QBoson Quantum Technology Co., Ltd.), Kai Wen (Beijing QBoson Quantum Technology Co., Ltd.)
10:40 - 10:45 What It Costs to Copy a Hybrid Quantum Head
Samanyu Goyal (California High School)
10:45 - 10:50 Error mitigation and fidelity certification share a fixed point
Aarush Pandey (Indian Institute of Technology Delhi), Inderpreet Kaur (Indian Institute of Technology Delhi), Monika Aggarwal (Indian Institute of Technology Delhi)
10:50 - 10:55 Trustworthy Machine Learning in the Real-Time Decoding Loop
Krishna Harish (Houston Community College), Ishant Ghimiray (Columbus State University)
10:55 - 11:00 CryptHarness: A Harness for Agent-Assisted Cryptanalytic Research
Nico Pelleriti (Technical University Berlin; Zuse Institute Berlin), Nicolai Kraus (Ruhr University Bochum), Alexander May (Ruhr University Bochum), Sebastian Pokutta (Technical University Berlin; Zuse Institute Berlin)
11:00 - 12:00 Panel Discussion: Secure and Trustworthy QML
12:00 - 14:00 Lunch and Poster Session
All accepted papers will be presented as posters for extended discussion and Q&A. View poster papers

Afternoon Session

14:00 - 14:30 Keynote Talk 4: Juan Cruz-Benito, IBM Quantum and IBM Research
14:30 - 14:35 Break
14:35 - 15:05 Keynote Talk 5: Soohyun Park, Sookmyung Women's University
15:05 - 15:10 Break
15:10 - 15:20 When Normalization Selects the Sign: Auditing Robustness Ablations in Quantum Attention
Owen Friedewald (University of Missouri), Srikar Alla (University of Missouri), Ali Shiri Sichani (University of Missouri), Chi-Ren Shyu (University of Missouri)
15:20 - 15:30 CRAE-GUARD: Clean-Referenced Absolute Evidence for Sampling-Robust Quantum Backdoor Detection
Zemin Chen (Old Dominion University), Yao Wang (Old Dominion University), Junrui Zhang (Old Dominion University), Lusi Li (Old Dominion University), Daniel Takabi (Old Dominion University), Rui Ning (Old Dominion University)
15:30 - 15:40 Shots Are Not Enough: Mask Coverage and Shot Precision in Adversarial Evaluation of Stochastic Quantum Neural Networks
Pablo Rivas (Marist University)
15:40 - 15:50 Where Quantum Fourier Sampling Stops Short: A Three-Gate Audit Protocol for Delay-PUF Security Models
Owen Friedewald (University of Missouri), Ali Shiri Sichani (University of Missouri), Chi-Ren Shyu (University of Missouri)
15:50 - 15:55 Adaptive-Shot Hybrid Quantum Anomaly Detection for Tactile Internet Security: Reliability-Aware Measurement Allocation Under Resource Constraints
Mubassir Serneabat Sudipto (Iowa State University), Shakil Ahmed (Grand Valley State University), Ashfaq Khokhar (Kansas State University), Samir M. Iqbal (Grand Valley State University)
15:55 - 16:00 Linter-Guided Repair of Quantum Code Smells
Greta Dolcetti (Ca' Foscari University of Venice), Giulio Zizzo (IBM Research), Liubov Nedoshivina (IBM Research), Juan Cruz-Benito (IBM Research), Nicolas Dupuis (IBM Research)
16:00 - 16:05 Four Forks: Evaluation Choices Decide the Conclusion in Meta-Learned Initialization for Variational Quantum Algorithms
Junyong Lee (Yonsei University), Seokhyeon Son (Yonsei University), Jeihee Cho (Yonsei University), Euimin Lee (Yonsei University), Hyeonseong Jung (Yonsei University), Daniel Justice (Carnegie Mellon University), Shiho Kim (Yonsei University)
16:05 - 16:10 Trustworthy Evaluation of Quantum Message Passing: Isolating Circuit Contributions in Traffic Forecasting
Firas Elhag (University of Tennessee at Chattanooga), Tam Bang (University of Tennessee at Chattanooga), Mina Sartipi (University of Tennessee at Chattanooga), Hoang H. Nguyen (University of Tennessee at Chattanooga)
16:10 - 16:15 When Error Mitigation Makes Things Worse: Budget-Aware Evaluation, Extrapolation Failure, and the Calibration Trust Boundary
Guilin Zhang (Workday AI Research), Kai Zhao (Workday AI Research), Xiquan Cui (Workday AI Research), Henry Heng (Workday AI Research), Xu Chu (Workday AI Research), Aletta Johanna Blanken (Workday AI Research)
16:15 - 16:30 Awards and Closing Remarks

Poster Papers

All accepted papers will participate in the poster session to facilitate extended discussion and Q&A. The papers listed below are poster-only presentations.

P01. When Selective Quantum Machine Learning Fails: A Cost-Aware Quantum-Kernel Bandit Audit for Human-AI Allocation
Luyao Zhang (Duke Kunshan University)
P02. SynQubi: An Agentic Framework for Trustworthy Quantum-Classical Financial Decision-Making with Generative Tail-Risk Scenarios
Chevin Jeon (Georgia Institute of Technology)
P03. vFedProtoQNAS: Prototype-Guided Personalized Quantum Neural Architecture Search for Virtual Federated Learning
Seok Bin Son (Korea University), Samuel Yen-Chi Chen (Wells Fargo), Soohyun Park (Sookmyung Women's University), Joongheon Kim (Korea University)
P04. Geometry-Aware Quantum Error Mitigation via Quantum State Matrix Approximation
Jioh Lee (Korea University), Sung Whan Yoon (UNIST), Joongheon Kim (Korea University)
P05. When Measurement Constraints Favor Quantum Computational Sensing for Stealthy Power-Grid Attack Detection
Saisubramaniam Gopalakrishnan (Phi Labs, Quantiphi), Supreeth B S (Phi Labs, Quantiphi), Pranav Sarda (Phi Labs, Quantiphi), Ashesh Xalxo (Phi Labs, Quantiphi), Dagnachew Birru (Phi Labs, Quantiphi)
P06. Cyber-Aware Quantum Network Control with Hybrid Quantum Anomaly Diagnostics and Risk-Aware Purification
Santanu Ganguly (OnQAI Ltd.; Quantum AI Research Group, Kingston University London)
P07. Dependency-Closed Evidence Contracts for Quantum Machine Learning Comparisons
Mateo Petel (Stanford University)
P08. Execution-Intent Integrity for Trustworthy Hybrid AI-Quantum Workflows
Sahar Daraeizadeh (QAILinks Technologies, Corp.)
P09. Does the Readout Bypass Leak the Input? A Feature-Visibility Audit of Hybrid Quantum-Classical Models
Guilin Zhang (Workday AI Research), Kai Zhao (Workday AI Research), Xiquan Cui (Workday AI Research), Henry Heng (Workday AI Research), Xu Chu (Workday AI Research), Aletta Johanna Blanken (Workday AI Research)
P10. On the Impact of Coordinate Descent for Multi-Angle QAOA in the Independent Set Graph Problem
Daeyeun Kim (Korea University), Seungcheol Oh (Korea University), Joongheon Kim (Korea University)
P11. From Pixels to Qubits: Quantum-Classical Fusion for Blockage Prediction in RIS-Aided Networks
Shakil Ahmed (Grand Valley State University), Ashfaq Khokhar (Kansas State University)
P12. Your Packets Are Showing: Hybrid Quantum Machine Learning for Passive OS Fingerprinting
Jae Sung Kim (Independent Researcher), La Alsulaim (University of Pittsburgh), Daniel Justice (Carnegie Mellon University / SEI)
P13. Investigating Impact of Quantum Ansatz Complexity in Quantum Hybrid Fusion Models to Estimate Protein-Ligand Binding Energy
Sanskriti Shindadkar (University of California, Berkeley), Clyde Villacrusis (UCLA Samueli School of Engineering), Manvi Agrawal (UCLA College of Letters and Science), Karen Mosayan (UCLA Samueli School of Engineering), Hayk Gargaloyan (UCLA College of Letters and Science), Nachiket Joshi (UCLA Samueli School of Engineering)
P14. Can You Trust Your Shot Budget? Benchmarking Conformal Risk Control for Variational Quantum Algorithms
Chandra Sekhar Dronavajjala (University of Connecticut)
P15. SecureQRC: Certifiable Temporal Robustness for Quantum Reservoir Computing in Network Intrusion Detection
Krishna Bhatia (QuantumAI Lab, Fractal Analytics), Smriti Bajaj (Dell Technologies)
P16. Mind the Handoff: Exploiting Classical-Quantum Gradient Interfaces in Quantum Reinforcement Learning
Asif Akhtab Ronggon (Louisiana State University), Tasnuva Farheen (Louisiana State University)
P17. Disentangling Noise and Epistemic Uncertainty in Quantum Machine Learning
Pascal Debus (Fraunhofer AISEC), Maximilian Wendlinger (Fraunhofer AISEC), Kilian Tscharke (Fraunhofer AISEC)
P18. Horizon-Conditioned Proposals for Rare-Event Estimation with Variational and Hybrid Quantum Circuits
Victoria Zhang (University of Illinois Urbana-Champaign; National Center for Supercomputing Applications), R. Srikant (University of Illinois Urbana-Champaign; National Center for Supercomputing Applications), Phuong Cao (University of Illinois Urbana-Champaign; National Center for Supercomputing Applications)
P19. Toward Joint Optimization of Circuit Depth and Training Data Size in Adaptively Grown Quantum Classifiers
Saeefa Rubaiyet Nowmi (Old Dominion University), Md Mahmuduzzaman Kamol (Old Dominion University), Mohammad Saidur Rahman (University of Texas at El Paso)
P20. Quantum Autoencoders for Anomaly Detection in Smart Grid Cybersecurity
Aditi Maheshwari (University of Toronto), Ahmad Mohammad Saber (University of Toronto), Deepa Kundur (University of Toronto)
P21. SGIQ: Sparse-Gradient-Informed Gradient Estimation for Variational Quantum Circuits
Yujia Cai (Rochester Institute of Technology), Zhe Li (Rochester Institute of Technology), Chaowen Guan (University of Cincinnati), Haibo Yang (Rochester Institute of Technology)
P22. Silent Metrics: Compiler-Dependent Resource Accounting and Its Consequences for Q-HPC Benchmarking and Scheduling
Romit Ghosh (Rutgers University-New Brunswick), Sharmin Afrose (Oak Ridge National Laboratory), Nicholas Stapleton (University of Tennessee, Knoxville), Ethan Seefried (Oak Ridge National Laboratory), Nirajan Banjade (Texas State University), Sangram Deshpande (North Carolina State University), Vicente Leyton Ortega (Oak Ridge National Laboratory), Travis S. Humble (Oak Ridge National Laboratory), Tirthankar Ghosal (Oak Ridge National Laboratory)

Invited Keynote Speakers

Swaroop Ghosh

Pennsylvania State University

Professor Muhammad Usman

Monash University, Australia

Samuel Yen-Chi Chen

Wells Fargo

Juan Cruz-Benito

Juan Cruz-Benito

IBM Quantum and IBM Research

Soohyun Park

Soohyun Park

Sookmyung Women's University

Invited Panel Speakers

Jean Utke

Jean Utke

Allstate

Kanad Basu

Kanad Basu

Rensselaer Polytechnic Institute

Tasnuva Farheen

Tasnuva Farheen

Louisiana State University

Aakash Kolekar

Aakash Kolekar

Senior AI Research Scientist, Amazon AI

Baber Javed

Baber Javed

Founder and CEO, Square63

Workshop Organizers

Saidur Rahman

University of Texas at El Paso

Jakub Szefer

Northwestern University

Taqi Raza

University of Massachusetts Amherst

Khoa Luu

University of Arkansas

Shahrooz Pouryousef

Chalmers University of Technology

Program Committee

  • Alberto Marchisio - New York University Abu Dhabi
  • Alvi Ataur Khalil - Southern Illinois University-Carbondale
  • Bibhas Adhikari - Fujitsu Research and Development Center Co. Ltm.
  • Cheng Chu - George Mason University
  • Christa Zoufal - IBM Quantum
  • Chuanqi Xu - Meta
  • Dan Rubenstein - Columbia University
  • Gang Qu - University of Maryland, College Park
  • Haribandhu Jena - National Institute of Science Education and Research
  • Jean-Pierre Seifert - Technical University Berlin
  • Jiafeng Xie - Villanova University
  • Joongheon Kim - Seoul National University Hospital
  • Junaid Farooq - University of Michigan - Dearborn
  • Juntao Chen - Fordham University
  • Luyao Zhang - Duke Kunshan University
  • Md Mahmuduzzaman Kamol - Old Dominion University
  • Miralem Mehic - University of Sarajevo, Faculty of Electrical Engineering
  • Muhammad Shafique - New York University
  • Nathan Kodama - Sygaldry Technologies
  • Nimisha Limaye - Synopsys Inc.
  • Raja Hasnain Anwar - University of Massachusetts at Amherst
  • Rui Ning - Old Dominion University
  • Saeefa Rubaiyet Nowmi - Old Dominion University
  • Sanjay Deshpande - Northwestern University
  • Satvik Maurya - University of Wisconsin-Madison
  • Shahrooz Pouryousef - Chalmers University of Technology
  • Shakil Ahmed - Iowa State University
  • Shengjie Xu - University of Arizona
  • Subhankar Mishra - National Institute of Science Education and Research
  • Tao Li - City University of Hong Kong
  • Theodoros Trochatos - Yale University
  • Tirtho Roy - Iowa State University
  • Vivek Harsha Vardhan Lakkamaneni - Amazon Science
  • Viyat Bhalodia - OpenAI
  • Wenqi Wei - Fordham University
  • Xinyi Li - Stevens Institute of Technology
  • Yifeng Peng - Stevens Institute of Technology
  • Ying Wang - Stevens Institute of Technology
  • Yingjie Lao - Tufts University
  • Yuwen Huang - The Hong Kong University of Science and Technology (Guangzhou)
  • Akash Kolekar - Amazon AI

Reviewers Instructions

SaTQuML 2026 accepts three submission types: Tiny Papers (2 pages), Short Papers (4 pages), and Long Papers (9 pages). Reviewers should evaluate each paper according to the expectations of its submission type rather than applying the same standard across all tracks.

Tiny Papers

Tiny papers are intended for focused, early-stage, or emerging ideas. Reviewers should assess whether the submission is scientifically sound, relevant to SaTQuML, clearly motivated, and potentially useful to the research community. Preliminary results, negative results, concise observations, benchmark ideas, open problems, and promising new directions are welcome. Tiny papers are not expected to provide the experimental depth or completeness of a full paper.

Short Papers

Short papers should present a meaningful and technically credible contribution with appropriate analysis or preliminary evidence where applicable. The work may still be developing and does not need the breadth of evaluation expected from a Long Paper. Reviewers should focus on scientific soundness, relevance, clarity, and whether the contribution can stimulate useful discussion or further research.

Long Papers

Long papers should be evaluated with a higher standard of rigor, technical depth, and completeness. Reviewers should consider the significance and novelty of the contribution, methodological soundness, adequacy of the evaluation, strength of evidence supporting the claims, comparison with relevant prior work, and clarity of presentation.

Review Philosophy

For Tiny and Short Papers, we encourage reviewers to be receptive to scientifically sound work that contributes useful ideas, evidence, or discussion to the SaTQuML community, even when the work is preliminary. Long Papers should meet a stronger bar for technical depth, evaluation, and completeness.

For all submission types, reviewers should assess whether the submission represents genuine scholarly work. Concerns about fabricated results or citations, inappropriate use of generative AI, plagiarism, or other research-integrity issues should be flagged to the Program Chairs with specific evidence rather than inferred solely from writing style.

Workshop Sponsors